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Drought risk mitigation in action and the Drought Resilience +10 conference






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    Book (stand-alone)
    Economic assessment of drought risk management
    A two-tier framework for cost–benefit analysis of proactive versus reactive drought management
    2024
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    Drought is a complex natural hazard, and the uncertainties surrounding its onset and impacts make investment decisions inherently difficult. On the other hand, drought is considered one of the costliest and most destructive natural hazards. With the threat of higher frequency and greater intensity of future drought events due to climate change, the debate in drought management has evolved from whether to implement reactive or proactive drought management approaches – in other words, whether to invest or not in proactive drought actions – to how to invest in proactive drought action. Different and evolving drought events can be mitigated with varying proactive measures, but the best trade-off between efficacy and profitability – be it a financial or an economic profit – must be targeted. The report investigates the broad concept of the economics of drought management, provides a conceptual, two-tier framework for the assessment of proactive and reactive actions, and disseminates case studies for the implementation of the framework in decision-making processes. This report aims to assist decision-makers, policymakers, planners, and national authorities responsible for planning and programming to conduct an exhaustive economic assessment related to drought. With the knowledge gained from the report, a critical step in the drought investment decision-making process can be effectively undertaken.
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    Article
    Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools 2024
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    To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify early signals of emerging food safety risks and to provide early warning in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things tools and methods as part of early warning and emerging risk identification in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments and tools increase the feasibility and effectiveness of prospective early warning and emerging risk identification, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

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